GRACeFUL lays a base for Rapid Assessment Tools (RAT), active DSS for collective policy making in global systems. RATs improve the policy-making process where multiple stakeholders are involved in multi-disciplinary, global challenges. A paradigm has been designed, adapting the discipline of Group Model Building (GMB).
In GMB sessions RATs provide support for interaction between the human process and existing scientific evidence. Digested evidence, like facts and knowledge about the real-world, is used to rapidly assess the effects of actions and the extent to which they solve a problem.
RATs employ solvers to propagate the effects of stakeholders’ targets, interests and constraints. The bridge between the social interaction and the underlying body of knowledge is achieved through modelling with a novel domain-specific language (DSL) of policy concept maps. Embedding this DSL into a programming language combining Functional Programming and Constraint Programming is a major breakthrough. The FP perspective empowers embedded DSL construction and improves scalability, verifiability and correctness of the models, while the CP approach introduces goal-directed problem-solving.
Management, understanding and modelling of complex systems is streamlined and opened up to diverse groups of stakeholders, hence promoting collective awareness. Although we focus on a selected problem, Climate Resilient Urban Design (CRUD), the approach is entirely generic, in view of future innovation opportunities.
The project aims at the following objectives:
(1) To establish a novel collective policy-making paradigm in which experts and stakeholders can participate in face-to-face problem-solving sessions. The framework will lie at the junction between GMB, simulation and problem solving.
(2) To carry out scientific work to bridge physical and mathematical models with high-level narratives. This will be achieved through the construction of a DSL for system dynamics models on top of a language that combines CP and FP and provides multi-scale solver models. Major work will be carried out in the study of ways to compose constraints and heuristics into higher-level language constructs.
(3) To design a visual layer for supporting the application of (1) coupled to the DSL from (2). This will include modes for visualizing spatial/temporal data, and manipulating cascading concept charts, causal loop diagrams, flow diagrams and constraint models. Interactivity will employ gamification to enhance user engagement.
(4) To enable the building of RATs supporting (1), using (2) and (3). This will be materialized in the form of DSL programming guidelines and a library of language constructs representing concepts common to many global challenges.
(5) To apply (4) in practice in the construction of a prototype for solving a specific real-world case of CRUD.
(6) To assess usability and user experiences of (5) and dissemination of results.
